Social Capital in the University-Based Innovation Ecosystems in the Leading Life-Science Clusters: Implications for Poland - ebook

Preface

Over the last decade, research in the field of technology and innovation has subsequently progressed toward the development of the notion of an ‘ecosystem’. Such an approach became particularly useful in understanding the dynamics related to the complex process of invention and its translation into an innovation which spreads further on into society and into what was highlighted in the latest Europe 2020 Growth Strategy and Cohesion Policy 2014–2020 as smart growth. The concept of an ecosystem lies within the idea that innovation and technological advances do not stem from the inventive efforts of one person, but rather collective research efforts and social interactions. In fact, smart growth starts from the bottom-up entrepreneurial discovery process about a region’s assets, its challenges, competitive advantages and the potential for excellence (European Commission 2012). In this approach of the priority setting of the region’s specialization, local dynamic externalities, social networks, and university-industry collaborations play a crucial role. The role of social collaboration networks seems to be even more important in the case of such dynamic industries as biotechnology where research is more complex and interdisciplinary.

The present monograph contributes to the emerging debate on the topic of innovation ecosystems by delivering new insights into and knowledge of the role of social capital, social networks and collaborative social environments in the successful life sciences innovation ecosystems. The authors applied a qualitative interview and direct observation methods which allowed to better understand the complex nature of the life sciences university ecosystem and more importantly, the process of social networking within it. The research study covered several well-established life sciences university-based ecosystems in the European Union and the United States. For the purpose of comparison, the study also considers Poland’s emerging life sciences sector. It is expected that the research findings, along with the recombination of the relevant subject literature and other collected empirical evidence, will make it possible to contribute to the development of strategies and policy measures to further unlock the innovation potential of the emerging life sciences sector in Poland.Introduction

The Triple Helix (TH) (university-industry-government interlinkages) approach to ‘innovation systems’ has been widely accepted, especially in the public sector. However, there has recently been an attempt to enrich this approach with a new concept of the Quadruple Helix (QH), which is grounded on the idea that innovation is the outcome of an interactive and trans-disciplinary process involving “all stakeholders as active players in jointly creating and experimenting in the new ways of doing things and creating new services and products” (European Commission 2015). Notably, the QH approach builds on the emerging concept of an ‘innovation ecosystem’ and widens the TH concept with one more helix – society and societal perspective (McAdam and Debackere 2018; Carayannis and Campbell 2012). Consequently, in the QH interactions, knowledge transfer among innovation actors is additionally strengthened by social, trust-based relations among the actors or so-called “social proximity”. The concept of an ‘innovation ecosystem’ refers to a network of interconnected organizations, connected to a focal firm or a platform that incorporates both production and uses side participants and creates and appropriates new value through innovation (Autio and Thomas 2014).

The life sciences industry, including biotechnology, is advancing at an unprecedented rate. As for 2018, the global life sciences sector accounted for approximately $1.6 trillion and was expected to reach over $2 trillion in gross value by 2023 (www.bisnow.com). Most of biotechnology research and industry innovation activities were concentrated in just few locations in the world. For example, San Francisco Bay Area is the largest recipient of the venture capital investments, along with the Boston-Cambridge area, and employs the highest share of biotechnology work force in the US (U.S. Life Sciences Clusters, 2019). In Europe, Cambridge (United Kingdom) life sciences is home to around 25% of Europe’s biotechnology companies and employs 57,000 people. It also accounts for 20% of the world’s Nobel Prize winners in medicine and chemistry (Cambridge Cluster 2019).

This high level of geographic concentration persists despite the subsequent rise in funding programs in the European Union to spur the development of the life sciences industry (Innovation Union Scoreboard 2018). In the last decade, another cluster in the north end of the United States – Seattle (Washington state) showed its incredible dynamics by becoming one of the fastest-growing life sciences market in the United States, with the rate of 16% growth on average in 2014–2017 (CBRE Research 2019). In Europe, a cluster on a cross-border region between Denmark and Sweden – the Medicon Valley – revealed its incredible scientific potential, which is reflected in the sharp increase of the volume of scientific publications in the life sciences – 23% between 2013–2016, and, to a lesser extent, in patent applications – 15% and 6% increase in Denmark and Sweden, respectively) (State of Medicon Valley 2018).

The success of these life sciences clusters poses questions as to which factors drove their success? There is a substantial amount of the high-tech-cluster-related literature considering the following success factors of the life sciences clusters: strong science and industry base, strong networks between industry and science, that facilitate the growth of both academic and industrial spin-offs, finance availability for new biotech companies (including venture capital and government funds), as well as traditions of local entrepreneurship (Maskell and Malmberg 2002; Su and Hung 2009). Relatively fewer sources mention the role of networks between faculty, investors, students, intermediary agents, and local authorities in sharing knowledge, information and thus stimulating inventions and innovations (Broekel and Boschma 2016; Ponds, Oort, and Frenken 2009; Audretsch and Feldman 2004; Audretsch and Stephan 1996; Adams 2002; Anselin et al. 1997; Golejewska 2018). The following study focuses on a relatively less discussed factor – social capital and social networks or larger social structures as a key determinant of the success of the life sciences ecosystem.

The core mission of the following study is to enable the reader to better understand the mechanisms and the significance of the networks and social capital in the selected sample of life sciences university-based ecosystems, as well as draw implications for the new emerging life sciences ecosystems in Poland. Thus, the study analyses the Triple (Quadruple) Helix networks within the life sciences ecosystems from a bottom-up perspective, by studying peoples` behaviour at the grassroots level. The study focuses on three major research problems: 1) the mission, structure and types of social networks; 2) the methods and the intensity of social networking/interactions as well as different dimensions of social capital; 3) the impact of social networks on R&D collaboration, innovative performance and future development plans.

In terms of methodology, most social science researchers acknowledge that the “social capital” and “social networks” are complex issues and therefore, they would benefit most from the integration of qualitative and quantitative approaches. In practice, however, effective quantitative research requires a larger sample size, which was not possible in the case of the following research study, due to the limited time and resources. Therefore, applying qualitative case-study research and direct observations were the best suited method to explore all sides of the social capital within the selected sample of life sciences clusters. The qualitative sample includes five case studies – life sciences ecosystems in San Francisco Bay Area (United States), Cambridge (United Kingdom), Copenhagen-Lund (Denmark/Sweden), Seattle (Washington State, United States) and Poland. The personal ‘interview’ technique was applied in order to collect in-depth content from the above ecosystems. The concept of a ‘university-based ecosystem’ was defined as a complex set of relationships among actors from universities and research institutes, enterprises, and other institutions, that lead to an inter-exchange of technology and information, and stimulate innovations. The broad goal of the interviews was to gain knowledge of and insights into how social interaction/networking fosters research collaboration and innovations. The questionnaire contained mixed questions (open and closed ones) and was composed of four parts: (1) the mission, structure and types of social networks; (2) the methods of networking and the intensity of interactions; (3) the role of different types of proximities in social networking; (4) the impact of social networks on R&D collaboration and innovative performance. The authors conducted interviews with the heads and deans of departments, the technology transfer offices (TTO), related educational institutions and companies in the following life sciences cluster ecosystems in the United Kingdom, the European Union and the United States. The list of all interviewed organizations is enclosed at the end of the paper. In order to analyze the evidence gathered, a multi-step thematic content approach was applied. The researchers transcribed the interviews to gain preliminary results, then looked for common and different patterns for all the analyzed ecosystems.

The present monograph is divided into seven chapters. The introduction is followed by a presentation of the theoretical and conceptual framework of social networks, social capital formation and university-based innovation ecosystems. The second chapter discusses major trends, developments and the role of technological convergence in the life sciences sector. The next four chapters discuss the life sciences clusters in Cambridge, Medicon Valley, the Bay Area and the metropolitan region of Seattle. The last chapter presents the life sciences cluster in Poland: its structure, important drivers and challenges. The monograph ends with important conclusions and implications for further studies and public policies.Chapter 1 Social Capital Formation and Its Role in the Cluster’s Innovation Ecosystem Małgorzata Runiewicz-Wardyn

1. Introduction

Firstly, the present chapter discusses the concept of social capital and its role in research collaboration, innovation networks in the high-tech clusters and innovation ecosystem contexts. Secondly, it point out the role of physical, cognitive, organizational, social and cultural distances in the stimulating knowledge and information exchange, with particular focus on social trust as an important element for the Triple (Quadruple) Helix networks. The present chapter aims to explore and profile the nature and dynamics of the Triple (Quadruple) Helix (government, university, industry, civil society) model as an enabler of social networks within the university-driven innovation ecosystems. Finally, the chapter discusses the role of different types and strength of social ties in the innovation ecosystems, as well as the role of intermediaries in the exchange of knowledge and information in the view of the subject-related literature.

2. Defining Social Capital

There is also an ongoing process of the institutionalization of the category of social capital as an important factor influencing the social, economic and technological development of regions. Various authors provide similar and slightly distinctive definitions of social capital. Social capital is related to broadly understood formal and informal relations between at least two people. Positive social capital creates relationships based on trust, cooperation, openness, etc., negative capital refers to the social relations that are characterized by the suspicion, hypocrisy and secretiveness (Walukiewicz 2007). In his comprehensive study, Nan Lin (2001) defines capital, as “an investment of resources with expected returns in the marketplace” (Lin 2001: 3). Furthermore, he identifies social capital with such “products” as trust, shared values, and norms. A similar link to the private benefit resulting from social capital was mentioned by Pierre Bourdieu (1986), defining social capital as a private investment in social networks that brings the owner expected benefits, such as wealth, and “symbolic capital” (social position). James Coleman (1988), in turn, regarded social capital as an individual good that could be, however, traded through social networks for the advancement of broader human capital. Finally, the last two decades witnessed many new studies extending social capital from the individual or private good to more of a collective or even public good. This group of researchers include Fukuyama (2001), Putnam (2000), Rosenfeld (2007), Lin (2001), Landabaso et al. 2007, Woolcock and Narayan (2000) and others. According to Francis Fukuyama (2002), social capital is a set of informal norms and rules as well as ethical values shared by individuals and social groups that enable them to cooperate effectively. For Robert Putnam, social capital does not belong to anybody, but is a public good representing a set of social norms and civic attitudes supporting common actions and trust for both interpersonal and in public institutions (Bochniarz and Faoro 2016). Social capital is defined by experts from the European Commission (2005) in a similar manner – “Social capital refers to those stocks of social trust, norms and networks that people can draw upon to solve common problems”. In turn, the World Bank defines it as a set of “institutions, relationships, attitudes and values that govern interactions among people and contribute to economic and social development” (Grootaert and van Bestelaer 2002). The institutional and relational context is also present in Roberto Camagni’s definition of social capital, which is “the set of norms and values which govern interactions between people, the institutions where they are incorporated, the relationship networks set up among various social actors and the overall cohesion of society (…). is the ‘glue’ that holds societies together” (Camagni and Capello 2012). The role of networks in the society was further extended in the comprehensive study by Franz Huber who proposes an interesting definition of social capital as “… resources embedded in social networks which can be potentially accessed or are actually used by individuals for action…” (Huber 2008: 19). Furthermore, he distinguishes “internal social capital” – resources mobilized through relationships between members of the collectivity – from “external social capital” – resources mobilized through relationships between members of the collectivity and actors outside of the collectivity. As an example of this dual character of social capital, Huber uses economic clusters, where the distinction depends on access to knowledge within the cluster and access to other clusters and outside individuals (Bochniarz and Faoro 2016). Philip Cooke adds the notions of reciprocity, trust and defines social capital “as the application or exercise of social norms of reciprocity, trust and exchange for political or economic purposes” (Cooke 2007: 102). He argues that knowledge-based industries are more engaged than others in building and performing social capital. Similarly, for Carlos Roman, social capital refers to a system of social relationships based on trust and working according to well-known rules (Landabaso et al., 2007). In turn, Stuart Rosenfeld interlinks the notion of social capital in clusters that gives opportunities to “know-who” to the notion of “know-how”. He also classifies social capital from the point of view of openness as positive and negative one (Rosenfeld 2007). Positive social capital creates economic advantages that are major forces for clustering. Negative social capital can develop when there are efforts to limit membership in clusters and cultivate insularity or “lock-in”. Finally, Cook and Rice (2006) in their chapter on the “social exchange theory” attempt to link social networks with social status, influence, solidarity, trust, affect and emotion. The authors emphasize the huge role of these connections and the macro-structures they create in the society.

To sum up, there is no common conceptual framework to the social capital concept. However, based on the above-quoted literature and for the purpose of the following study, social capital is defined as a type of capital that results from investments in building relations, institutions and networks that produce collaborative attitudes, shared norms and values as well as mutual understanding and trust.

2.1. Social Network Without or With “Closure”

The attempts to conceptualize social capital have resulted in the identification of many different types and characteristics of social capital in the literature. The most common ones refer to the distinction of bonding and bridging, as well as structural and cognitive social capital (Halpern 2004). Bonding social capital is between individuals within a group or community (horizontal ties), whereas bridging is between individuals and organizations in different communities (vertical ties) (Dolfsma and Dannreuther 2003; Narayan 2002). Bonding social capital is related to thick trust, while bridging social capital is closely related to thin trust (Anheier and Kendall 2002).

Most of the literature refers to the Granovetter’s (1992) introduced division between between the ‘structural’ and ‘relational’ social capital. The first one conforms to the view that social capital constitutes aspects of social structure, and therefore relates to the properties of the social system and the form of social organization. It is the network relationships, but not the quality of these relationships, since the quality of relationships is the relational dimension. Structural social capital facilitates access to the exchange and transfer of knowledge and makes it easier for people to engage in mutually beneficial collective action by lowering transaction costs and improving social learning (Uphoff and Wijayaratna 2000; Ansari, Munir, and Gregg 2012; Andrews 2010). Relational social capital refers to the nature, characteristics and quality of the relationships within networks, such as trust, obligations, respect and even friendship (Lefebvre et al. 2016; Gooderham 2007; Cabrera and Cabrera 2005).

Furthermore, extending the major themes initiated by the studies of Coleman (1988, 1990) and Burt (2000) on social capital, it is important to distinguish the networks ‘with closure’ or ‘without closure’. The argument for social capital with closure is that it creates strong interconnected elements, and the environment in which everyone is connected (dense network) is the source of social capital (bonding social capital). Coleman (1990) claims that social relations can save time by accessing direct information from different actors. Moreover, according to Coleman, network closure “facilitates sanctions that make it less risky for people in the network to trust” (Burt 2000). Thus, he argues that networks with a closed structure are better at facilitating social capital, as demonstrated in Figure 1.1(a), than social networks characterized by an open structure, which is illustrated by Figure 1.1(b).

Figure 1.1. Social network without and with “closure”

Source: Coleman (1988).

Burt (1992), who introduced the concept of structural hole in networks, argues, on the contrary, that low density and connectivity are the most beneficial features of a social network. He claims that social capital is created by a network in which people can broker connections (“bridging capital”) (2000). Structural holes mean that an individual has persons in his or her network that do not know each other, and this is defined as “a relationship of non-redundancy between two contacts”, which is illustrated by the hole between contacts in a network that do not have any relationship with each other. This way, that person is more likely to have access to so-called non-redundant information, i.e. information that is fresher and more unique. In turn, Coleman concludes that the quality of information may in fact deteriorate as it moves through different chains of intermediaries. Notwithstanding, Burt (2000) resolves this disagreement in such a way that dense or hierarchical networks lower the risk associated with transaction and trust, whereas the hole argument describes how structural holes are opportunities to add value with brokerage across the holes.

Moreover, Granovetter (1973) distinguishes between strong and weak ties and states that the strength of a social tie is defined by a combination of the time invested, the emotional intensity, the intimacy or mutual confiding between the actors. In other words, ties with a higher degree of emotional involvement are more important in the discovery of a business opportunity, and weak ties become more important when exploiting these opportunities. The described relationship would look as follows: if A has ties with B and A has ties with C, then the amount of time that C spends with B depends (at least in part) on the amount of time that A spends with B and C, respectively. If C and B have no relationship, common strong ties to A will probably bring them into interaction and generate one. Granovetter (1973) refers to that as “the strength of weak ties”. The propensity of two nodes that are indirectly connected to form a link is also referred to as the “triadic closure” in the literature (Carayol et al. 2014). The “triadic closure” networks (collaboration with a partner of a partner) are particularly advantageous for international collaborations, in which reliability of different partners may be difficult to assess.

In the context of cluster ecosystem interlinkages, strong ties describe strong relationships, based on trust and are characterized by frequent interaction (both formal and informal one) which lead to a greater exchange of knowledge (Burt 2009; Rowley et al. 2000). At the same time, weak ties could potentially add heterogeneity to the knowledge base of cluster actors.

Table 1.1 presents the classification of the characteristics of bonding and bridging social capital based on the above-presented literature.

Table 1.1. Bonding vs. bridging social capital

------------------------ -------------------------
Bonding social capital Bridging social capital
Within Between
Closed Open
Inward looking Outward looking
Horizontal Vertical
Strong ties Weak ties
Thick trust Thin trust
Network closure Structural holes
------------------------ -------------------------

Source: Ramos-Pinto (2012).

Notwithstanding, the classification made above may lead to an overly simplified and even contradictory image of the social capital networks. In practice, social relationships are far more complicated and usually accompanied by multiple overlapping relationships that individuals have with each other. Thus, a typical relationship would have some characteristics of bonding and some characteristics of bridging social networks. Last but not least, bonding and bridging are not completely mutually exclusive and the final structure of the network configuration depends on the type of knowledge interlinkages present in a particular cluster, its technological dynamics, as well as the importance of other dimensions of social capital, i.e. physical, cognitive, organizational, cultural and communication ones.List of tables

Table 1.1. Bonding vs. bridging social capital

Table 3.1. Number of Bachelor’s or equivalent level graduates in biological and related sciences in the International Standard Classification of Education (ISCED) 2011, level 6

Table 3.2. Number of doctoral or equivalent level (ISCED 2011, level 8) in biological and related sciences

Table 3.3. Top total life sciences graduates by US school (2018)

Table 3.4. Top total life sciences doctorates by US school

Table 4.1. Cambridge technology transfer data, 2017–2018

Table 5.1. Copenhagen University’s and Lund University’s technology transfer data, 2018

Table 6.1. Selected technology transfer data, 2017 (*2018)

Table 7.1. The technology transfer of the University of Washington, 2018–2019

Table 8.1. The Jagiellonian University’s and Warsaw University’s technology transfer data, 2017–2018

Table 8.2. Summary of the basic features, differences and similarities of all five case studiesList of figures

Figure 1.1. Social network without and with “closure”

Figure 1.2. The role of social capital in Triple Helix networks

Figure 1.3. Role of intermediaries in the Triple Helix networks

Figure 2.1. Technological change and technological convergence in the life sciences industry

Figure 2.2. Technological convergence and multidisciplinary approach in the life sciences

Figure 2.3. Product innovation life cycle in the biopharmaceutical sector

Figure 3.1. Patent registrations for new medicines by region, in thousands, 2005–2018

Figure 3.2. European biotechnology financing by year, 2001–2016

Figure 3.3. Innovation capital raised by leading European countries, 2016

Figure 3.4. US life sciences venture capital funding

Figure 3.5. Innovation capital raised by leading US regions, 2016

Figure 3.6. Clinical trials initiated in Europe, the United States and other countries during 2014–2019, per one million inhabitants

Figure 4.1. The growing rank of the University of Cambridge in the life sciences sector, 2011–2019

Figure 4.2. Cambridge University’s overall scores in the categories Life Sciences, and Clinical, Pre-clinical & Health

Figure 5.1. The growing rank of the University of Copenhagen in the biological sciences, 2011–2019/2020

Figure 5.2. The University of Copenhagen’s overall scores in the categories Life Sciences, and Clinical, Pre-clinical & Health, 2019

Figure 5.4. Lund University’s overall scores in the categories Life Sciences, and Clinical, Pre-clinical & Health, 2019

Figure 6.1. The rank of Stanford University (a) and the University of California, Berkeley (b), in the life sciences sector, 2011–2020

Figure 6.2. The overall scores of Stanford University (a) and the University of California, Berkeley (b), in the categories: Life Sciences and Clinical, Pre-clinical & Health

Figure 7.1. The growing rank of the University of Washington in the life sciences sector, 2011–2019

Figure 7.2. The overall scores of the University of Washington in the Life Sciences category and the Clinical, Pre-clinical & Health category, 2019

Figure 8.1. Number of clinical trials in Poland, 2003–2017

Figure 8.2(a). The Jagiellonian University’s overall scores in the life sciences and the category Clinical, Pre-clinical & Health, 2019

Figure 8.2(b). Warsaw University’s and Adam Mickiewicz University’s overall scores in the life sciences ranks, 2019

Figure 8.3. Patent applications and patents granted by the Jagiellonian University, 2007–2019

Figure 8.4. Patent applications and patents granted by University of Warsaw in 2007–2019